Affective computing is the study and development of systems and devices that can recognise human emotions. This can be done using sensing technologies and AI algorithms to process biological signals or facial images to identify the different affective states, such as happiness, anger, fear, surprise, sadness and disgust. This non-invasive technique has applications in healthcare such as emotional impairment detection, mental health assessment, emotional stress assessment, cognitive decline detection, attention deficit disorders, neurodegenerative diseases, neurological disorders, autism spectrum disorder, stress, anxiety or other behavioural assessment.

• Chapter 1: EEG Based Recognition Using Time-Frequency Images and Hybrid ResNet Models
• Chapter 2: Detection of Facial Emotion using Thermal imaging based on Deep Learning Techniques
• Chapter 3: Gender and Emotion Recognition from EEG & Eye-movement Patterns
• Chapter 4: Gesture-Oriented SuperNumerary Robotic Fingers for Post-Stroke Rehabilitation
• Chapter 5: Comparative Analysis of CNN and RNN-LSTM Model Based Depression Detection Using Modified Spectral and Acoustic Features
• Chapter 6: Unraveling Emotions: Harnessing Pre-trained Convolutional Neural Networks for Electroencephalogram Signal Analysis
• Chapter 7: Deep Neural Network-based Stress Detection using Biosignals
• Chapter 8: Explainable Deep Learning Models for Emotion Recognition using Facial Images
• Chapter 9: The convergence of emotion recognition with the Internet of Artificial intelligence things